Services / 06
TURN OPERATIONAL DATA
INTO INFRASTRUCTURE FOR DECISIONS.
Overview
Model the business, not the export
Dimensional models reflect how the organisation actually operates, so metrics stop contradicting each other.
- Tested pipelines
- Freshness, volume and schema assertions run with every load and fail loudly before anyone reads a wrong number.
- Separation of concerns
- Analytical workloads live away from transactional systems, protecting product performance.
Data Engineering — reference architecture
Business problems addressed
WHEN COMPANIES CALL US.
Reporting is assembled by hand from systems that disagree with each other.
Analytics queries run against production and slow the application down.
Data quality issues are discovered by the people reading the dashboard.
Machine-learning work is blocked by the absence of dependable datasets.
Engineering capabilities
WHAT THIS ENGAGEMENT COVERS.
- 01
ETL/ELT
- 02
Data pipelines
- 03
Data modelling
- 04
Data warehouses
- 05
Operational analytics
- 06
Streaming
- 07
Database architecture
- 08
Data quality
- 09
Data integration
Typical engagements
- Warehouse and modelling layer that becomes the single source of truth
- Streaming pipeline for near-real-time operational visibility
- Data quality and lineage programme across existing pipelines
Technology expertise
- PostgreSQL
- dbt
- Airflow
- Kafka
- BigQuery
- Snowflake
- Python
- Spark
Technologies listed reflect production experience, not certifications or vendor partnerships.
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